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Conformal Prediction

Conformal Prediction is a machine learning framework that provides valid measures of confidence for individual predictions. It offers a principled approach to quantify uncertainty in predictions without assuming any specific distribution for the data. This section features papers that explore various aspects of conformal prediction, including theoretical advancements, algorithmic developments, and applications across different domains.

Papers

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Showing 491–500 of 704 papers

TitleStatusHype
Self-supervised conformal prediction for uncertainty quantification in Poisson imaging problems—0
Semiparametric conformal prediction—0
Semi-Supervised Conformal Prediction With Unlabeled Nonconformity Score—0
Distribution-free Conformal Prediction for Ordinal Classification—0
Universality of conformal prediction under the assumption of randomness—0
Conformalized Prediction of Post-Fault Voltage Trajectories Using Pre-trained and Finetuned Attention-Driven Neural Operators—0
Single Trajectory Conformal Prediction—0
Smart Surrogate Losses for Contextual Stochastic Linear Optimization with Robust Constraints—0
SPARC: Prediction-Based Safe Control for Coupled Controllable and Uncontrollable Agents with Conformal Predictions—0
An Information Theoretic Perspective on Conformal Prediction—0
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